Method for determining a characteristic data record for a data signal

ABSTRACT

According to the method according to the invention for determining a characteristic data set (“fingerprint”) for a sound signal, the sound signal itself is searched through for characteritsic locations, and these characetristics locations are used for producing a characteritsic data set. For this the frequency spectrum is evaluated over a time interval, subdivided into frequency bands and averaged over each frequency band into a value. The fingerprint then consists of data which has been obtained from these values after possible further averagings, wherein only data is included which belongs to certain time segments

[0001] The present invention relates to a method, to a computer program, to a recording medium and to a device for determining a characteristic data set for a data signal, as are defined in the independent claims.

[0002] One of the consequences of the rapid development of the world wide web is the on-line obtainability and sales of music recordings which are duplicated as pirate copies, which opens the door to copyright infringement on a large scale. The detection of such infringements until now has not been effected systematically and was more or less left to chance. A reason for this is the fact that the identification of music recordings by bugging is time consuming and therefore the companies and persons hit by copyright infringements had to make considerable means available. On the other hand music identification based on computer software has until now been limited to indicators such as the length of the recording or characteristic combinations on a recording. Such an identification is suitable for the comparison of original CD recordings, but however fails if a recording has been clipped, compressed or changed in another manner. For example it thus particularly does not function with the identification of digital downloads, analog recordings etc.

[0003] Analogous problems are also encountered with further electronic data for which one may raise the question of a copyright infringement. Thus for example film recordings are becoming increasingly digitally available, for example recorded on a DVD or may be downloaded online in a suitable format.

[0004] These questions which arise in the field of copyright are part of a large problem. The handling of large data quantities is generally a constantly growing challenge. Ever increasing quantities of data are electronically recorded and made available. Compression algorithms indeed help reduce these data quantities for transmisison and storage. However the much greater basic underlying problem of handling the data, that of finding one's way in the quantities of information, is not solved by way of this. One group of examples of such large data quantities amongst others are to be found in technology, for example with the electronic monitoring of the condition of a system, for example of a motor, an installation, a vehicle, etc. Here it would be desirable if in a short time one could obtain important information on the condition of the monitored system from a large quantity of arising information. This analogously applies for example also to science and its direct application. Whole branches of research are essentially occupied with evaluating recorded data during a short time, particular in astronomy, in particle physics, when determining the structure of biological matter. The same is also true of medicine (computer tomography, nuclear spin tomography, electronically recorded X-ray pictures, ultrasopund data recording etc.).

[0005] It is here where the present invention comes in. It is to create the possibility of being able to allocate to a data signal which is present in the function of a parameter, a characteristic data set which is characteristic of the data signal when it is not exactly recorded (for example dubbed in an analog manner), has been electronically changed and/or clipped. This data set is to permit an efficient and automated comparison of the data signal to a reference. The comparison should yield conclusive information as to whether the data signal is essentially identical to the reference.

[0006] It is therefore the object of the invention to provide a method for determining a characteristic data set for a data signal as well as a computer programm, a recording medium and a device for carrying out this method, wherein the method is to be effected in an automised manner and the data set is to be as small and as managable as possible and is to be characteristic of the data signal even if the data signal has not been exactly recorded, has been electronically changed and/or clipped.

[0007] The object is achieved by a method, a computer program, a recording medium and a device as are defined in the independent patent claims.

[0008] With the method according to the invention for determining a characteristic data set for a data signal, the data signal itself as a function of the parameter is searched through for characteristic locations, and these characteristic locations are used for the production of a characteristic data set.

[0009] A first example of such a data signal is a sound recording which is present as a parameter as a function of time. The characteristic locations may then for example be selected as characteristic with respect to their frequency distribution, their rhythm, their volume and/or the temporal change of these variables.

[0010] In this embodiment of the method according to the invention thus sound recordings themselves are processed into a characteristic data set. This is in contrast to the state of the art where a data set characterising a sound recording, on the sound carrier, is added to the sound recording. By way of this the characterising data set is characteristic of the sound recording, thus is for example the piece of music itself and is not dependent on possible electronically manipulatable data such as the length of the recordings, their placing on a recording medium (CD, DVD, . . . ) etc. The method according to the invention at the same time has an amazing parallel to the manner in which a person identifies a music recording. An analogous consideration may be made also for example with film recordings or results of measurements etc.

[0011] By way of the method according to the invention and the device according to the invention, for exampe a data set of at the most a few kilobytes is allocated to a data signal which digitalised comprises a data quantity of several megabytes. It is now ascertained that this data set determined according to the invention is characteristic of the music recording and capable of differentiating it. On account of the obvious analogy, the data set in the subseqeunt description is thus also called a “fingerprint”.

[0012] The method according to the invention may run its course in a fully automised manner and at no point in time is an approximation or a decision of a person required. By way of this and on account of the scope of the data quantity which is very small in comparison to the actual data signal and which preferably comprises a fingerprint, the method is very suitable for managing large data banks. This applies also to the use in the world wide web which has developed into something like a single worldwide data bank.

[0013] By way of programs which automatically sift through the world wide web (webcrawlers, “spiders”, etc.), the method according to the invention may be used to search through this for sound and video recordings. For this, only a data bank of fingerprints created according to the invention and belonging to a music or film publisher needs to be present, which is compared to the continuously detected fingerprints of sound or film recordings offered in the web. These may then for example with other means be examined whether they are pirate copies. The method according to the invention, the computer program, the recording medium and the device may however also serve other purposes. Thus for example an unknown sound recording may be identified and delivered by a music publisher. Radio stations too are often confronted with the requirement of ascertaining the identity of a recording by which has been sent in by a listener and see a potential in the invention for economising their activity. A further application of the invention could be a billing system for pieces of music or film recordings marketed via the internet. Finally the invention may also serve for searching larger music or film data banks for duplicates or, before a new entry, to examine such a data bank whether the corresponding recording is not already present.

[0014] Hereinafter the invention is described in more detail by way of one embodiment example in the field of “music recordings”. The drawings at the same time serve for illustration. In the drawings

[0015]FIG. 1 shows an input signal,

[0016]FIG. 2 the Fourier transformed signal of a time interval,

[0017]FIG. 3 the logarithmically scaled signal of FIG. 2 subdivided into 9 bands,

[0018]FIG. 4 a representation of the signals of 8 bands in each case averaged over the whole band, as a function of time

[0019]FIG. 5 a representation of a sequence of data in each case averaged over a time segment of 0.4 s, consisting in each case of 9 data points,

[0020]FIG. 6 a representation as FIG. 5, wherein the data of the four time segments determining the fingerprint are represented in a bold manner,

[0021]FIG. 7 an overview of a method for determining a characteristic time domain,

[0022]FIG. 8 a characteristic data set determined according to the invention, as a quantity of discrete values, as a function of time and frequency,

[0023]FIG. 9 the values of FIG. 8, together with an average surface, and

[0024]FIG. 10 the average surface of FIG. 9 together with the representation of angles setting its position.

[0025] An input signal which may for example be transformed into music, a human voice or another sound signal is brought into a processable standard form in a first step. For this it is digitalised or decompressed, converted from a stereo into a monosignal when required and subsequently led to the device via an interface. The device input data accordingly is present as a simple signal A(t) as a function of time t as is is shown in FIG. 1 of the drawings.

[0026] The method is now based on scanning through the sound signal, determining its frequency spectrum in a multitude of intervals and reducing the data quantity by a data processing apparatus, thus a computer, to a few data points. Subsequently a certain time domain is selected. The data points representing the frequency spectrum over this time domain serve as a “fingerprint” of the sound signal. In the following one possibility for determining the fingerprint is yet described by way of one example with advantageous numbers and time interval lengths.

[0027] The frequency spectrum A(f) (FIG. 2) of the signal in the interval i is obtained from the signal A_(i)(t) in this interval by way of a Fourier transformation. In the cited example the signal recorded over an interval length of 25 ms is numerically Fourier transformed. The Fourier transformation is effected by a known numerical method, for example by the FFT (Fast Fourier Transform) algorithm. The frequency spectrum A_(i)(f) which is thus obtained is subsequently logarithmically scaled. This generally at the same time has a smoothing effect on the spectrum. The spectrum is subsequently subdivided into 9 bands which on the logarithmic frequency scale for example have a uniform width, as this is also shown in FIG. 3. In each band there is now effected a weighted averaging of the A_(i)(f) signal over the bandwidth to a value A_(i,n), wherein n=1, 2, . . . , n₀=9. The average values A_(i,n) serve as characteristic values for the frequency spectrum in the band n in the interval i. Fourier transformation, scaling and averaging are effected simultaneously to the scanning through of the sound signal. In this manner the quantity of the data to be stored and processed in the data processing apparatus is kept small at all times.

[0028] A representation of the values A_(i,n) as a function of the interval indexes representing time for each band n=1, 2, . . . , n₀=8 as results of an actual measurement are shown in FIG. 4. For each of the curves the minimum of the value also represents the zero line.

[0029] In a next step a further averaging over time is effected. For this, for each band n the values A_(i,n) of the respective 16 intervals i of a time segment z of 0.4 s duration are averaged into a value A_(i,n). This averaging too may be effected simultaneously to the scanning through of the sound signal. FIG. 5 represents a sequence of eight data of a time segment consisting in each case of n₀=9 data points.

[0030] After the sound signal has been scanned through and the above described process has been completetly finished the fingerprint is evaluated from the end data A_(z,n). For this in a first step in each case four neighbouring time segments z are grouped together to a time domain of 1.6 s length. A certain value is evaluated for each time domain, for example the sum of 4×9=36 values A_(z,n), the sum of the differences of the first and of the last A_(z,n)-value for each band n or likewise. The evaluated value serves the identification of a location in the sound recording. The fingerprint consists of the 36 values A_(z,n)z=k, . . . , k+3; n=1, . . . ,9 of that time domain for which the evaluated value is extreme thus for example the sum is maximal (FIG. 6).

[0031] The evaluation of the time domain selected for the fingerprint does not need to be effected directly or indirectly from information determined from the the values A_(i,n). Alternatively to this, the data signal as a function of time may also be used for determining such a time domain. For example a vicinity of the volume maximum is selected as a time domain. The characteristic data set analogously to the previously described example is composed of the values A_(z,n) in a vicinity of the maximum.

[0032] A refinement of the procedure for determining a characteristic location in the data signal is yet described by way of FIG. 7. In FIG. 7 the energy as a function of time is shown schematically. This curve may for example originate directly from the signal. It may however also be obtained according to the previously described averaging method via the A_(z,n)-values, which in the example of FIG. 6 serves for determining the time domain. According to the figure a local maximum is determined. If subsequent to reaching the maximum, the value of the energy reduces by a configurable value H, a vicinity of this value is used for determining a fingerprint. Thereafter one searches for a local minimum. As such likewise only one after which the energy increases by the value H (or another configurable value) is valid. In the figure a minimum is further drawn in, after which the value only increases by h<H. This value is not used as a minimum. The value at which the minimum is reached for example is not used for a fingerprint. On the other hand only after this is a maximum again evaluated at which the next fingerprint is determined. In the figure the used extremes are indicated with arrows.

[0033] By way of this procedure one may synchronise the recognition of data signals. At each point in time one may access into a stream and then take the fingerprints at the locations which have already been used for determining the fingerprint of the reference.

[0034] Although the original data signal was only given as a function of a parameter—the time, the thus determined fingerprint is a function of two discrete variables z and n. By way of this one may achieve a recognition quota which is far superior to a corresponding method which only records data points as a function of one parameter. This is true in comparison to a method which envisages determining individual volume values as a function of time as well as also for determining a frequency spectrum at a certain poimnt in time. The increased recognition quota is also the case if for example a number of volume values is selected which is comparable to the number of data points A_(z,n).

[0035] Here it is expressly pointed out that of course one may just as easily select interval lengths, bandwidths, etc. which are different from those in the embodiment example. For example the number of used bands no is preferably at least between 4 and 40 for example between 6 and 16. The number of time segments is preferably likewise at least 4 and may for example lie between 8 and 64. e.g. at 16. The total number of data points which form the fingerprint for example is at least 24. The length of the time interval over which the fingerprint is determined is preferably at least 0.05 s and at the most 60 s.

[0036] A representation of the characteristic data set of a data signal with a parameter selection different from the previous description is shown in FIG. 8. The figure shows values A_(z,n) as a function of z, according to time and the band n, according to the (logarithmised) frequency. In the example according to FIG. 8 at the same time there are selected 8 frequency bands with in each case 8 checkpoints. The 64 data points form the fingerprint and represent a “mountain”.

[0037] By way of FIG. 9 there is further shown how the characteristic data set of FIG. 8 may be further reduced. An average surface as a middle plane of the points is shown dashed in the figure. It is for example determined by the method of least squares. As known a plane may be completely characterised by 3 parameters, for example by the x, y and z axis sections in a Cartesian coordinate system. Alternatively to this, according to the invention the average position of the plane (height) as well as the angle in the frequency direction and in the time direction may be used as parameters. FIG. 10 serves for clarifying the significance of the angles; they are a measure of the local course of volume and the local frequency distribution.

[0038] The three parameters height, angle in the frequency direction and angle in the time direction are no longer used as a fingerprint for the data signal, but serves its indexing. Such an indexing is recommended with large data quantities, where the comparison of a fingerprint to all reference fingerprints of a data bank may lead to problems in performance. Fingerprints may be sorted by way of the indexing. If a comparison of a fingerprint to the values of a data bank is to be effected, the indexing of the data bank signals is compared to the corresponding indexing values of the signal to be compared. One selects those fingerprints of the databank for which the angles only slighly deviate from that of the fingerprint to be compared, for example plus/minus a percentage number such as plus/minus 35%. The height is preferably not used for indexing since it is dependent on the scaling. A further usable index is the “hilliness” of the mountain, i.e. the average square deviation of the points of the mountain from the surface. The thus selected fingerprints are compared to the fingerprint to be compared.

[0039] For the sake of simplicity only one of the above values may be used as an index.

[0040] A comparison of the fingerprint of two different sound signals is effected now as follows. According to whether the sound signals have been scaled or not, in a first step the fingerprint is normalised. All values A_(z,n) of each of the two fingerprints are multiplied by a constant so that after this multiplication the sums of the 36 values A′_(z,n) and B′_(c,n) of both fingerprints are equal to a predetermined number, for example 1. Thus sound recordings of different volumes may therefore also be compared. If on the other hand it is clear that the two signals have not been scaled, then a normalisation should not take place since it entails a loss of information. The sum of the squared differences A′_(z,n)−B′_(c,n) is formed in a pointwise manner from the possibly normalised fingerprints. If these do not exceed a certain threshhold value the sound recordings characterised by the fingerprints are identical. It is yet to be mentioned here that this method yields a fuzzy-match. The obtained information “identical”/“not identical” depends on the selected threshhold value and is not strictly unambiguous, wherein however the recognition quota is extremely high.

[0041] The determining of the fingerprint for data signals which are given as a function of a single parameter (for example time) functions analogously to the above method for sound signals. The data signal in a short interval is Fourier transformed with respect to the parameter t and the “spectrum” obtained by way of this is averaged over a finite number of bands to a finite number no of values. The bands for example on a logarithmic scale have a uniform width. This method is carried out for a plurality of intervals following one another, by which means one obtains no discrete functions from the interval index i or from the parameter t. Thereupon there are for example effected averagings over several intervals (i) to values A_(z,n) provided with a further index z. A characteristic location is then determined as a function of the parameter t or of the index i or z. As such one selects a location where a certain value is extreme. The fingerprint then consists of the values A_(z,n) for n=1, . . . ,n₀ and z in the vicinity of the extreme value. Also with this embodiment form, by way of subdividing the parameter domain into a multitude of intervals i and Fourier transformation in these intervals it is achieved that the characteristic data is present as a function of two indices z and n or as a discrete function of the parameter t as well as a corresponding variable in the Fourier domain. The fingerprint is thus given as a quasi 2D function which is gained from a 1D function.

[0042] For many signals time lends itself as a suitable parameter. That which has been Fourier transformed as a frequency spectrum then also has a illustrative significance. The method may however also be applied to other parameters. With a signal representing a picture one may also determine a corresponding spectrum for a location parameter, for example by Fourier transformation.

[0043] With the example “film recording” it will be briefly discussed how this method may be extended to functions with several variables. The video signal of a film recording may for example be represented as h(x, y, t), wherein x,y is a co-ordinate system representing the display surface and t the time. Analogously to the above method there is effected a Fourier transformation with respect to time. In principle this is effected for all values x, y. Preferably however in the x and y direction one carries out a suitable averaging for reducing the data quantity. For averaging functions of the type h_(t)(x, y) one refers to the extensive know-how of electronic picture processing. The following steps are again effected analogously to the above method.

[0044] Concluding, the method for determining a characteristic data set for a data signals lies in analysing the data signal itself, processing it further and retaining a part as a characteristicc data set. For this the sound signal is preferably digitalised in the case that it is present in a non-digital form, and subsequently analysed by way of electronic data processing and processed into a characteristic data set, so that the data quantity of the characteristic data set is a multiple smaller than that of the data signal. This may be effected by carrying out the following steps:

[0045] subdividing the data signal into intervals i and time segments z,

[0046] determining a spectrum in each interval i by transforming the signal into the frequency domain,

[0047] extraction of values A_(i,n) characteristic of the spectrum, for each interval,

[0048] determining values A_(i,z) characteristic of the spectrum from the values A_(i,n), for each time segment,

[0049] setting a set of time segments whose values A_(i,z) form the characteristic data set.

[0050] The determination of the spectrum by transforming the signal into the frequency domain in the above described examples is effected by a numeric Fourier transformation. However also alternative known or yet to be developed methods may be applied for transforming a signal into the frequency domain, for example a Hartley transformation, a series of (digital) electronic filters, etc.

[0051] The extraction of the characteristic values A_(i,n) is for example a weighted averaging over a frequency band. Each time segment may consist of one or of several intervals so that the determination of the values A_(z,n) is effected by averaging the values A_(i,n) over the intervals of the time segment.

[0052] Preferably in each case one group of time segments neighbouring one another are grouped together for setting the set of time segments. Subsequently a certain value is evaluated for each time domain, for example the sum of the 36 values A_(z,n) or the sum of the differences of the first and of the last A_(z,n)-value for each band n. That group of time segments for which the evaluated value is extremal, thus for example the sum is maximal, forms the set of time segments.

[0053] A data processing program for determining a characteristic data sets for a sound signal is for example a program which enables a computer to carry out the above described method step by step. A recording medium for this program is for example a computer hard disk prerecorded with this program, a CD or a DVD, an electro-optical computer disk, an external magnetic memory, a computer floppy disk or another storage medium. A device for determining a characteristic data set for a sound signal for example is a computer equipped with a data processing program.

[0054] Concluding, it is further pointed out that the above described method in no way represents the only conceivable embodiment form of the invention, but may yet be modified in some aspects. Thus as already mentioned completely different interval and time segment lengths are conceivable than those which have been previously described. In particular it is also possible for each time segment to correspond exactly to one time interval, thus that for the values A_(i,n) not to be averaged further. Assessments other than the previously described effected setting of the values A_(z,n) any evaluation of an extreme value are also conceivable for determining the set of time intervals. An unambiguous identification of a location in the data signal is the only thing that matters here.

[0055] Considerations which are significantly different from methods previously described as examples are also conceivable. Thus for example important method steps such as transforming the signal into the frequency domain or averaging may also be carried out in an analog-electronic manner. For example the evaluation of extremes may be carried out very well in the known analog-electronic manner. A device for carrying out the method according to the invention with electronic means has for example means for electronic Fourier transformation, for electronic integration and switch means which are actuated on reaching the maximum or threshhold values of the signal. Furthermore for example an A/D converter is present which allocates a digital value to the function values which have been evaluated on actuating the switch means. Storage means, for example a computer connected via an interface, and EEPROM memory or likewise may serve for storing the characteristic data set. A comparison to a reference which for example has likewise been stored may be effected in an analog-electronic manner. The Fourier transformation/logarithmisation in such a device may be replaced by a series of bandpass filters. In this embodiment form the bandpass filters ensure a transformation of the signal into the frequency domain.

[0056] Another variation of the above embodiment examples may be achieved if the domain which is selected as the characteristic location in the data signal is not coherent. For example one may envisage this domain to comprise several time spans which in each case are for example distanced from one another by half a minute, one minute . . . In this manner amongst other things one may unambiguously differentiate an individual clip from a piece of music or a film, for example a trailer, from the whole piece of music. 

1. A method for determining a characteristic data set for a data signal which is present as a function of time, wherein the data signal itself is searched through for characteristic locations and these characteristic locations are used for producing a characteristic data set, with the following steps: setting time intervals i transforming the data signal in the intervals i into the frequency domain averaging the thus obtained frequency spectrum over frequency ranges into discrete values A_(i,n) for each interval i selecting a time domain as a characteristic location by way of information gained from the data signal or the values A_(i,n) wherein the time domain comprises several intervals i, and evaluating characteristic data from the values A_(i,n) of the intervals lying in the time domain.
 2. A method according to claim 1, characterised in that the time domain as the characteristic location is selected as characteristic with respect to the frequency distribution, the rhythm, the magnitude of the amplitude of the data signal and/or the temporal change of these variables.
 3. A method according to claim 1 or 2, characterised in that the data signal is digitalised in the case that it is present in a non-digital form, and that it analyses by way of electronic data processing and is processed into the characteristic data set, wherein the data quantity of the characteristic data set is a multiple smaller than that of the data signal.
 4. A method according to claim 3, comprising the following steps: subdividing the data signal into intervals and time segments z, determining a frequency spectrum in each interval i, extraction of values A_(i,n) characteristic of the frequency spectrum in the interval i, for each interval i, determining values A_(z,n) characteristic of the frequency spectrum in the time interval z from the values A_(i,n,) for each time segment, setting a set of time segments forming the time domain, whose values A_(z,n) form the characteristic data set.
 5. A method according to claim 4, characterised in that the frequency domain is subdivided into several frequency bands, wherein the width of the various frequency bands preferably in a logarithmic scale is identical, and that the extraction of the characteristic values A_(i,n) is a weighted averaging over each frequency band n.
 6. A method acording to claim 4 or 5, characterised in that each time segment consists of one or of several intervals.
 7. A method according to claim 6, characterised in that the determination of the values A_(z,n) is effected by averaging the values A_(i,n) over the intervals of the time segment.
 8. A method according to one of the claims 4 to 7, characterised in that for setting the set of time segments, in each case one group of time segments neighbouring one another are grouped together, that from the values A_(z,n) of the time segments of each group the sum or a sum of differences is formed, and that that group of time segments at which the thus evaluated value is extreme forms the set of time segments.
 9. A method according to one of the claims 4 to 8, characterised in that for setting the set of time segments one determines a first local maximum of a variable determined from the data signal as a function of time, that one ascertains whether this variable subsequently to reaching the first local maximum reduces by a first configurable value (H), that if this is the case, at least one time segment in the vicinity of the first local maximum is determined as belonging to the set of time segments, that subsequently one searches for a local minimum of the variable following which the variable increases by a second configurable value (H) and that one searches for a second local maximum of the variable following this minimum and at least one time segment lying in a vicinity of this second local maximum is determined as belonging to the set of time segments.
 10. A method according to one of the preceding claims, characterised in that the transformation of the signal into the frequency domain is carried out with a numeric Fourier transformation or with a Hartley transformation.
 11. A method according to one of the preceding claims, characterised in that at least one of the method steps is carried out in an analog-electronic manner.
 12. A method for determining a characteristic data set for a data signal, which is present as a function of a parameter t, wherein the data signal itself is searched through for characteristic locations and these characteristic locations are used for producing a characteristic data set, with the following steps: setting parameter intervals i, transforming the data signal with respect to the parameter t in the intervals i, into a corresponding frequency domain, averaging the thus obtained spectrum to discrete values A_(i,n) for each interval i, selecting a parameter range as a characteristic location by way of information obtained from the data signal or the values A_(i,n), wherein the parameter range comprises several intervals i, and evaluating characteristic data from the values A_(i,n) of the intervals lying in the parameter range.
 13. A method for recognition of a data signal which is present as a function of time using a method according to one of the claims 1 to 11, wherein the data signal is searched through for characteristic locations and these characteristic locations are used for the production of a characteristic data set which is compared to reference data sets from a data bank, with the following steps setting time intervals i transforming the data signal in the intervals i into the frequency domain averaging over frequency ranges the thus obtained frequency spectrum into discrete values A_(i,n) for each interval i selecting a time domain as a characteristic location by way of information gained from the data signal or the values A_(i,n), wherein the time domain comprises several intervals i, and evaluating characteristic data from the values A_(i,n) of the intervals lying in the time domain determining a deviation variable of the characteristic data from the reference data sets, for example as the sum of the square deviations selecting that reference data set with which the deviation size is minimal.
 14. A method according to claim 13, characterised in that the characteristic data is compared to all reference data sets present in the data bank.
 15. A method according to claim 13, characterised in that from the characteristic data one determines index data, that index data is likewise allocated to the characterisic data sets, and that only those data sets are used for the comparison, for which the deviation of the index data from the index data of the characteristic data does not exceed a relative or absolute threshhold value.
 16. A method according to claim 15, characterised in that the index data is determined by way of the following method steps: determining an average surface of the characteristic data set as that linear function of time and of the logarithmically scaled frequency, for which the sum of the square deviations from the values of the characteristic data set is minimised, determining parameters characterising the average surface, for example two angles defining its position, as index data.
 17. A storage medium with a computer program stored thereon, which comprises means to permit a computer to carry out methods for determining a characteristic data set for a data signal which is given as a function of a parameter, for example time, with the following steps: setting parameter intervals i numerically determining a spectrum of the data signal in the intervals i averaging the thus obtained spectrum into discrete values A_(i,n) for each interval i selecting a parameter range as a characteristic location by way of information gained from the data signal or from the values A_(i,n) wherein the parameter range comprises several intervals, and evaluating characteristic data from the values A_(i,n) of the intervals lying in the parameter range.
 18. A computer program with means to permit a computer to carry out a method for determining a characteristic data set for a data signal which is given as a function of a parameter, for example time, with the following steps: setting parameter intervals i numerically determining a spectrum of the data signal in the intervals i averaging the thus obtained spectrum into discrete values A_(i,n) for each interval i selecting a parameter range as a characteristic location by way of information gained from the data signal or from the values A_(i,n), wherein the parameter range comprises several intervals, and evaluating characteristic data from the values A_(i,n) of the intervals lying in the parameter range.
 19. A computer program product with a computer-readable medium containing computer-readable program code means in order to permit a computer to carry out a method for determining a characteristic data set for a data signal which is given as a function of a parameter, for example time, with the following steps: setting parameter intervals i numerically determining a spectrum of the data signal in the intervals i averaging the thus obtained spectrum into discrete values A_(i,n) for each interval i selecting a parameter range as a characteristic location by way of information gained from the data signal or from the values A_(i,n), wherein the parameter range comprises several intervals, and evaluating characteristic data from the values A_(i,n) of the intervals lying in the parameter range. 